Transparent Local Features for Object Recognition

Despite the omni-presence of transparent objects in our daily environment, little research has been conducted on how recognize and detect such objects. The difficulties of this task lie in the complex interactions between scene geometry and illuminants that lead to changing refractory patterns. Realizing that a complete physical model of these phenomena is out of reach at the moment, we seek different machine learning solution to approach this problem.
In particular we investigate a latent local additive feature model. In stark contrast to previous approach, this method seeks to separate different contributions to the overall gradient statistic in an unsupervised decomposition approach.